Analysis Of Shock Waves Research Articles

Prediction of queue length due to lane closure with shockwave analysis on Jagorawi toll road (case study: Km 19+600)

This study discusses the prediction of queue length with shockwave analysis through traffic flow characteristics on the Jagorawi toll road KM 19+600. The purpose of this study is to analyze the relationship between traffic characteristics (current, density and speed) on the Jagorawi KM19+600 toll road and evaluate the value of shock waves due to the narrowing of the road on the Jagorawi KM 19+600 toll road when the lane is closed. The method used to achieve the purpose of this study is to use the linear regression method to determine the relationship between traffic variables through the Greenshield model. The data obtained was obtained from CCTV data recording at KM 19 + 600. The results of the current-velocity-density relationship analysis show that the selected model for the relationship is the Greenshield model with a maximum current of 9574 pcu/hour. From the results of the shock wave simulation for the closure of 1 lane, 2 lanes and 3 lanes with the same flow, namely 7200 pcu/hour. It can be known the length of the queue that occurs, where the length of the queue that occurs depends on the volume of traffic, the length of the closure and the number of lanes that are closed.

Energy-load Matching and Shockwave Analysis of Electrical Explosion

Electrical explosion shockwave is a technique for inducing formation resonance by the interruption of formation pressure balance. By matching complex nonlinear influencing factors, this paper optimizes the engineering parameters and estimates the influence range of the shockwave. It achieves the objective of efficient shockwave acquisition and amplitude control. The test apparatus rapidly discharges the stored energy of various capacitance-voltage schemes, causing the load of various diameter-length structures to generate shaped shockwaves. By characterizing the discharge circuit and the shockwave signal, the energy and load matching was studied. Extraction of shockwave signal characteristics for decomposition and reconstruction analysis. The matching results indicate that the 10 MW loop oscillation is generated within 5 μs of discharge time, with the load diameter structure getting a major effect on the deposited energy. The 50 MPa shaped shockwave is generated in a 100 mm diameter region. And the peak pressure is influenced primarily by the load length structure and voltage scheme. The optimal load structure is D = 0.2 mm and L = 25 mm, controlling 2 to 5 times the input power’s enthalpy and increasing the voltage to create the shockwave more efficiently. The results of shockwave analysis indicate that the signal’s time-domain characteristics correspond precisely with its propagation path. The wavelet packet analysis indicates that the shockwave possesses a broad frequency and a high peak. The detection signal is composed of the principal shock wave, the radiation pressure wave, and the resonance error. The results provide theoretical basis for engineering application.

LWR and shockwave analysis - Failures under a concave fundamental diagram and unexpected induced disturbances

This paper undertakes a detailed empirical study of traffic dynamics on a freeway. The results show the traffic dynamics that systematically determine the shape of the fundamental diagram, FD, can also violate the stationarity assumptions of both shockwave analysis and Lighthill, Whitham and Richard's models, thereby inhibiting the applicability of these classical macroscopic traffic flow theories. The outcome is challenging because there is no way to identify the problem using only the macroscopic detector data. The research examines conditions local to vehicle detector stations to establish the FD while the single vehicle passage method is used to analyze the composition of vehicles underlying the aggregate samples. Then, traffic states are correlated between successive stations to measure the actual signal velocities and show they are inconsistent with the classical theories. This analysis also revealed that conditions in one lane can induce signals in another lane. Rather than exhibiting a single signal passing a given point in time and space, the induced and intrinsic signals are superimposed on one another in the given lane. We suspect the subtle dynamics revealed in this research have gone unnoticed because they are far below the resolution of conventional traffic monitoring. The findings could have implications to other traffic flow models that rely on the FD, so care should be taken to assess if a given model is potentially sensitive to the non-stationary dynamics presented herein.The results have a direct impact on practice. Traffic flow theory is a critical input to many aspects of surface transportation, e.g., traffic management, traffic control, network design, vehicle routing, traveler information, and transportation planning all depend on models or simulation software that are based upon traffic flow theory. If the underlying traffic flow theory is flawed it puts the higher level applications at risk. So, the findings in this paper should lead to caution in accepting the predictions from traffic flow models and simulation software when the traffic exhibits a concave FD.

Evaluating a signalized intersection performance using unmanned aerial Data

ABSTRACT This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.

Global shockwaves of the Hunga Tonga-Hunga Ha’apai volcano eruption measured at ground stations

The eruption of the Tonga volcano created globally propagating spherical shockwaves in the atmosphere. Analyses are done to data from two southern U.S. stations of the author sampling at 3-21s intervals and 189 weather stations at 1-5min intervals. The shockwaves arrived from two routes in the atmosphere: the shortest spherical arc and the longer spherical arc through the antipole. In most stations, signals up to the 6th path of shockwaves were recorded as the waves traveled around the globe multiple times. The speed of shockwaves is estimated to be 309.5± 2.9m/s, consistent with the speed of sound at the top of the troposphere where a waveguide exists. Discussion is made on the post-shockwave ringing of 4-8min as higher amplitude oscillations above the level of pre-shockwaves background noise. A theoretical wave dispersion is derived which verifies that the spherical shockwave's phase speed is the same as the speed of sound.

A general framework for combining traffic flow models and Bayesian network for traffic parameters estimation

This work focuses on traffic parameters estimation based on trajectory data in an arterial corridor with multiple signalized intersections. We develop a general framework that can combine various traffic flow models with Bayesian Network (BN) for estimating the overall traffic parameters using partially observed vehicle trajectory data (with unknown penetration rate). The BN is formulated to establish the probabilistic relationship between the traffic arrival process, traffic states, traffic flow model parameters and observed vehicle trajectories. More specifically, given traffic arrival information (e.g., traffic arrival volume) and fundamental diagram parameters (e.g., capacity, jam density, and free flow speed), vehicle trajectories are derived or simulated based on traffic flow modelling (e.g., shockwave analysis, Cell Transmission Model (CTM), or microscopic traffic simulation model VISSIM). Here, the extracted entry time of an observed vehicle at a pre-defined location upstream of the signal and its travel time are used to establish the probabilistic relationship. On the other hand, they are also the input parameters of the model for the estimation. Then, by combining a dynamic traffic flow model with Bayesian inference, we develop a framework to establish the learning process for traffic parameters estimation, such as traffic volume and traffic flow model parameters. The proposed framework is evaluated with different traffic flow models using the NGSIM dataset of an arterial corridor with three signalized intersections. For the CTM-BN model, the mean absolute percentage error (MAPE) of the estimation is generally below 5% when the penetration rate is above 12%. Regarding the VISSIM-BN model, the MAPE of the estimation is generally below 5% when the penetration rate is above 6%. These results demonstrate the applicability of the framework even under a relatively low penetration rate, and that the fidelity of the dynamic traffic model used does influence the estimation performance.

Comparing COVID-19 with the GFC: A shockwave analysis of currency markets

I analyze the shockwave effect of the COVID-19 pandemic on currency markets, with a comparison to the global financial crisis (GFC), employing Kapetanios m-break unit root test, investigations of standalone risk measures—downside variance, upside risk, volatility skewness, Gaussian Value at Risk (VaR), historical VaR, modified VaR—and Diebold–Yilmaz volatility spillover analysis. Standalone risk analysis shows that the turmoil in the initial months of COVID-19 was not as severe as that in the GFC. However, examination of co-movements and volatility spillovers illustrates a different scenario. According to the results of the static connectedness measure of Diebold–Yilmaz, the shockwave of the COVID-19 pandemic in the total volatility spillover is about eight times greater than that of the GFC. Among standalone risk measures, the results closest to this finding are obtained from volatility skewness analysis. Additionally, of six foreign exchange rates, the Brazilian real and Turkish lira are the currencies experiencing the greatest increase in received volatility during the GFC and the COVID-19 pandemic, respectively. These findings suggest the severe effect of crises on emerging financial markets.

Combining shockwave analysis and Bayesian Network for traffic parameter estimation at signalized intersections considering queue spillback

This paper focuses on traffic parameters estimation at signalized intersections based on a framework combining shockwave analysis (SA) and Bayesian Network (BN) using vehicle trajectory data. Detailed queuing evolution and spillback across adjacent intersections are considered. According to shockwave analysis, the analytical probability distribution of individual vehicle’s travel time is derived based on different initial conditions. This probability distribution is parameterized by the fundamental diagram (FD) parameters, traffic volume, and cycle state (queue length). A three-layer recursive BN model is then proposed to construct the state evolution process as well as the relationships between traffic volume, cycle state, FD parameters, sampled vehicles’ arrival times and intersection travel times. As traffic volume and initial queue cannot be measured directly from sampled trajectory data, the expectation maximization (EM) algorithm and particle filtering (PF) are introduced to solve this recursive BN model. By shockwave analysis, such estimated traffic parameters are then used to estimate the maximum queue length and traffic volume of each cycle. The proposed method is evaluated using microscopic traffic simulation data as well as empirical data. Numerical results show that the proposed method achieves promising accuracy even under low penetration rates, with the mean absolute percentage error (MAPE) of the estimation bounded by 15% and generally around 10%.

Journal Vol – 15 No -7, July 2020 Journal > Journal > Journal Vol – 15 No -7, July 2020 > Page 6 PERFORMANCE AND EMISSION CHARACTERISTICS OF GASOLINE-ETHANOL BLENDS ON PFI-SI ENGINE Authors: D.Vinay Kumar ,G.Samhita Priyadarsini,V.Jagadeesh Babu,Y.Sai Varun Teja, DOI NO: https://doi.org/10.26782/jmcms.2020.07.00051 admin July 26, 2020 Abstract: Alcohol based fuels can be produced from renewable energy sources and

Journal Vol – 15 No -7, July 2020 Journal > Journal > Journal Vol – 15 No -7, July 2020 > Page 6 PERFORMANCE AND EMISSION CHARACTERISTICS OF GASOLINE-ETHANOL BLENDS ON PFI-SI ENGINE Authors: D.Vinay Kumar ,G.Samhita Priyadarsini,V.Jagadeesh Babu,Y.Sai Varun Teja, DOI NO: https://doi.org/10.26782/jmcms.2020.07.00051 admin July 26, 2020 Abstract: Alcohol based fuels can be produced from renewable energy sources and

Toward the Formalization of Macroscopic Models of Traffic Flow Using Higher-Order-Logic Theorem Proving

Next-generation transportation will be integrated, interconnected and highly autonomous. One key challenge in traffic management is ensuring safety while maintaining the required level of service quality. In such future mobility systems, rigorous formalization and validation will thus become critical to ensure that the transportation network operates as intended, traffic properties are reliably maintained, and services resume in a timely manner after potential disruptions. Formal methods have recently gained considerable attention as a modeling and verification paradigm capable of addressing many challenges associated with next-generation transportation systems. In this regard, we propose to use higher-order-logic theorem proving for formally analyzing transportation systems. As a first step towards this direction, we present a logical framework for the formal analysis of macroscopic models of traffic flow. Leveraging upon the high expressiveness of the underlying logic, we formally model the continuous components of macroscopic models while capturing their real behavior. In particular, we present a formalization of some foundation concepts of macroscopic models, namely density, flow rate, mean speed, relative occupancy, and shockwave using the higher-order-logic theorem prover HOL Light . This choice is primarily motivated by the fact that the macroscopic models deal with the traffic flow dynamics and thus play a vital role in planning strategies in allocating resources for implementing optimized and balanced transportation systems. For illustration, we perform the formal input-output and shockwave analysis of a German freeway. The case study demonstrated the practicability of this formal approach due to the high expressiveness of the underlying logic. The proposed research is first step towards formalizing the foundational mathematical theories and core concepts of traffic flow theory. This accomplishment will open new ways to plan and model various components of the transportation systems such as highway links, ramp metering, merging behavior and eventually address the problem of routing vehicles in a network of automated vehicles.

Scaling spray penetration at supersonic conditions through shockwave analysis

In the current paper, an investigation of the supersonic flow effect on shockwave generation and diesel spray penetration scaling has been performed. For this purpose, spray visualization tests have been carried out in a constant-pressure chamber at room temperature using shadowgraphy technique. Two working gases have been used: nitrogen, with similar thermodynamic characteristics to the engine environment, and sulfur hexafluoride, aimed at producing supersonic conditions at moderate injection pressure values. A total of 60 operating points, including different nozzle geometries, injection pressures and chamber densities have been studied. From the visualization study, two different kinds of shockwaves have been detected: normal or frontal, for moderate spray tip Mach (between 1 and 1.5); and oblique, when the Mach is higher than 1.5. The penetration results show that, for the same injection conditions in terms of injection pressure and chamber density, the spray propagation is equal for SF6 and N2 when the spray is on subsonic conditions, while penetration is higher for SF6 when supersonic velocity is reached. This behavior has been related to the density gradient appearing across the shockwave. A new methodology to extrapolate supersonic penetration from the well-known subsonic penetration law has been proposed, showing good agreement with the experimental results.

Traffic parameters estimation for signalized intersections based on combined shockwave analysis and Bayesian Network

This paper proposes a framework to combine shockwave analysis (SA) with Bayesian Network (BN) for traffic flow parameters estimation at signalized intersections using vehicle trajectory data. Traffic SA is applied as the basis to derive the analytical expression of individual vehicle trajectory given the fundamental diagram (FD) parameters (e.g., capacity, jam density, free flow speed) as well as the traffic volume. According to SA, the analytical probability distribution of each vehicle’s travel time (defined as the time of a vehicle travelling between an upstream and a downstream location at a signalized intersection) is derived. This probability distribution is parameterized by traffic volume and FD parameters. We then apply a three-layer BN model to construct the relationship between traffic volume, FD parameters, sampled vehicles’ arrival times and intersection travel times. Since traffic volume cannot be measured directly from sampled vehicle trajectories, an expectation maximization (EM) algorithm is introduced to perform learning for estimating the cycle-by-cycle volume (or arrivals) and FD parameters. Such estimated parameters are then used, via SA, to estimate vehicles’ intersection travel times. The proposed combined SA-BN model is evaluated using microscopic traffic simulation data and the NGSIM dataset. Sensitivity analysis of the estimation performance with respect to penetration rates is conducted. In general, the proposed method yields promising estimation accuracy, with the mean absolute percentage error (MAPE) of the estimation bounded by 15%, generally around 5%, as compared with the true value, even under low penetration rates.

Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System

In the cloud computing era, the analyses of various types of big data gathered from the Internet of Vehicles, the Internet of Things, and smart sensors/devices achieve convenient services. Some famous applications include vehicle traffic loading on roads (from Google MAP), video sharing for fans group (from Facebook or Youtube), and so on. However, to achieve real-time active safe driving (RT-ASD) under unstable driving in high-threat areas on roads becomes an open issue in cloud computing-based intelligent transportation system. This paper proposes a predictive backward shockwave analysis approach (PSA) to achieve the RT-ASD based on the analyses of macroscopic traffic shockwave ( PSA_MA ) and microscopic car-following ( PSA_mi ). The PSA contributes in several aspects to active safe driving: 1) predicting and analyzing high threat of backward shockwaves from the gathered big data of the driving state information vehicles; 2) informing the analyzed threat messages to the vehicles in high-threat areas via the 3-Tier hierarchical cloud computing mechanism; 3) reducing the driving threat certainly; and 4) PSA_MA and PSA_mi can be applied for achieving active safe driving in autonomous self-driving vehicles and human-driving vehicles. The numerical results show that the PSA outperforms in approaches to relative error rate prediction, the accuracy of the backward shockwave determination, average vehicle velocity, average travel time, number of goodput vehicles, time-to-collision, and distance-to-collision.

EFFECTS OF PAVEMENT SURFACE DEFORMATIONS ON TRAFFIC FLOW

Pavement surface deformations have a significant effect on speed profile of vehicles and traffic flow conditions. These deformations limit driving properties and increase vehicle operation and maintenance costs. Additionally, they cause many problems such as accidents, slower movement speeds, capacity loss and severe discomfort states. There are many factors having an effect on road capacities and they vary according to different road and traffic flow conditions. In this study, it is aimed to investigate and develop models to estimate shockwave and bottleneck forming, capacity loss and speed reduction, which occurred on examined road links caused by pavement deformations. For the prediction of road capacity, flow–density (q–k) relationship, bottleneck and shockwave analysis methods were used. In the scope this study, deformed road links were divided into three sections; Section A – before deformation zone, Section B – deformation zone, and Section C – after deformation zone. All three sections were investigated and empirical results were obtained. According to analysis results, it was found that pavement surface deformations have a negative effect on the level of road service capability. Obtained results also showed that there are significant reductions in capacity relatively by up to 44 and 26% would result from surface deformations on deformed lanes and non-deformed adjacent lanes.

An Adaptive Signal Control Scheme to Prevent Intersection Traffic Blockage

In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.

Real-time queue estimation model development for uninterrupted freeway flow based on shockwave analysis

In this study, the authors developed a time-space discrete macroscopic model based on the shockwave theory for real-time queue estimation in uninterrupted freeway flow, using fixed-location loop detector data. After investigating the queue characteristics both at an active bottleneck and within a variable speed limit control case, the proposed model was applied to these two cases on Whitemud Drive, a major freeway corridor in Edmonton, Alberta, Canada. Modified Highway Capacity Manual–based methods were used to determine queue density in uninterrupted freeway flow. The effect of time interval size on queue estimation was studied, as loop detector data acquisition frequencies may differ. It was found that the proposed model accurately estimates real-time queue length independent of the time interval. Multiple single queues were implemented in a calibrated VISSIM 5.3 micro-simulation model to perform the validation task. The study is a helpful foundation for future active traffic management strategy development and improvement.

Queue Profile Estimation in Congested Urban Networks with Probe Data

AbstractQueues at signalized intersections are the main cause of traffic delays and travel time variability in urban networks. In this article, we propose a method to estimate queue profiles that are traffic shockwave polygons in the time‐space plane describing the spatiotemporal formation and dissipation of queues. The method integrates the collective effect of dispersed probe vehicle data with traffic flow shockwave analysis and data mining techniques. The proposed queue profile estimation method requires position and velocity data of probe vehicles; however, any explicit information of signal settings and arrival distribution is indispensable. Moreover, the method captures interdependencies in queue evolutions of successive intersections. The significance of the proposed method is that it is applicable in oversaturated conditions and includes queue spillover identification. Numerical results of simulation experiments and tests on NGSIM field data, with various penetration rates and sampling intervals, reveal the promising and robust performance of the proposed method compared with a uniform arrival queue estimation procedure. The method provides a thorough understanding of urban traffic flow dynamics and has direct applications for delay analysis, queue length estimation, signal settings estimation, and vehicle trajectory reconstruction.

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

Maxwell and Cattaneo’s Time-Delay Ideas Applied to Shockwaves and the Rayleigh-Bénard Problem

We apply Maxwell and Cattaneo's relaxation approaches to the analysis of strong shockwaves in a two-dimensional viscous heat-conducting fluid. Good agreement results for reasonable values of Maxwell's relaxation times. Instability results if the viscous relaxation time is too large. These relaxation terms have negligible effects on slower-paced subsonic problems, as is shown here for two-roll and four-roll Rayleigh-Benard flow.

Application of a dynamic-mixture shock-wave model to the metal–matrix composite materials

a b s t r a c t The so-called dynamic mixture model is applied to a prototypical metal matrix composite (MMC) system (consisting of an aluminum matrix and SiC particulates) in order to investigate the propagation of planar (i.e. one directional), longitudinal (i.e. uniaxial strain), steady (i.e. time-invariant) structured shock waves. Waves of this type are typically generated during blast-wave loading or ballistic impact and play a major role in the way blast/ballistic impact loads are introduced into a structure. Hence, the knowledge of their propagation behavior is critical for designing structures with superior blast and impact protection capacities. To validate the computational procedure used, the structured shock-wave analysis is first applied to a homogeneous (i.e. single component) metallic system (commercially pure niobium). Next, the analysis is applied to the aforementioned MMC (in the limit of intermediate to strong shocks) when the contribution of the stress deviator to the total stress state can be neglected. Finally, the computational results are compared with their experimental counterparts available in the open literature in order to validate the dynamic-mixture method used.